VAIT: A visual analytics system for metropolitan transportation

Siyuan Liu, Jiansu Pu, Qiong Luo, Huamin Qu, Lionel M. Ni, Ramayya Krishnan

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

With the increasing availability of metropolitan transportation data, such as those from vehicle Global Positioning Systems (GPSs) and road-side sensors, it has become viable for authorities, operators, and individuals to analyze the data for better understanding of the transportation system and, possibly, improved utilization and planning of the system. We report our experience in building the Visual Analytics for Intelligent Transportation (VAIT) system, which is the first system on real-life large-scale data sets for intelligent transportation. Our key observation is that metropolitan transportation data are inherently visual as they are spatio-temporal around road networks. Therefore, we visualize and manage traffic data, together with digital maps, and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAIT on real-world taxi GPS and meter data sets from 15 000 taxis running for two months in a Chinese city of over 10 million people. We discuss the technical challenges in data calibration, storage, visualization, and query processing and offer first-hand lessons learned from developing the system. Based on our extensive empirical experiment results, VAIT beats state-of-the-art methods and systems in terms of scalability, efficiency, and effectiveness and offers us an easy-to-use, efficient, and scalable platform to shed more light on intelligent transportation research.

Original languageEnglish (US)
Article number6522454
Pages (from-to)1586-1596
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume14
Issue number4
DOIs
StatePublished - Dec 1 2013

Fingerprint

Global positioning system
Query processing
Scalability
Visualization
Availability
Calibration
Planning
Sensors
Experiments

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

Liu, Siyuan ; Pu, Jiansu ; Luo, Qiong ; Qu, Huamin ; Ni, Lionel M. ; Krishnan, Ramayya. / VAIT : A visual analytics system for metropolitan transportation. In: IEEE Transactions on Intelligent Transportation Systems. 2013 ; Vol. 14, No. 4. pp. 1586-1596.
@article{35137e848a9a4997a43af9a0736bcc6c,
title = "VAIT: A visual analytics system for metropolitan transportation",
abstract = "With the increasing availability of metropolitan transportation data, such as those from vehicle Global Positioning Systems (GPSs) and road-side sensors, it has become viable for authorities, operators, and individuals to analyze the data for better understanding of the transportation system and, possibly, improved utilization and planning of the system. We report our experience in building the Visual Analytics for Intelligent Transportation (VAIT) system, which is the first system on real-life large-scale data sets for intelligent transportation. Our key observation is that metropolitan transportation data are inherently visual as they are spatio-temporal around road networks. Therefore, we visualize and manage traffic data, together with digital maps, and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAIT on real-world taxi GPS and meter data sets from 15 000 taxis running for two months in a Chinese city of over 10 million people. We discuss the technical challenges in data calibration, storage, visualization, and query processing and offer first-hand lessons learned from developing the system. Based on our extensive empirical experiment results, VAIT beats state-of-the-art methods and systems in terms of scalability, efficiency, and effectiveness and offers us an easy-to-use, efficient, and scalable platform to shed more light on intelligent transportation research.",
author = "Siyuan Liu and Jiansu Pu and Qiong Luo and Huamin Qu and Ni, {Lionel M.} and Ramayya Krishnan",
year = "2013",
month = "12",
day = "1",
doi = "10.1109/TITS.2013.2263225",
language = "English (US)",
volume = "14",
pages = "1586--1596",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

VAIT : A visual analytics system for metropolitan transportation. / Liu, Siyuan; Pu, Jiansu; Luo, Qiong; Qu, Huamin; Ni, Lionel M.; Krishnan, Ramayya.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 4, 6522454, 01.12.2013, p. 1586-1596.

Research output: Contribution to journalArticle

TY - JOUR

T1 - VAIT

T2 - A visual analytics system for metropolitan transportation

AU - Liu, Siyuan

AU - Pu, Jiansu

AU - Luo, Qiong

AU - Qu, Huamin

AU - Ni, Lionel M.

AU - Krishnan, Ramayya

PY - 2013/12/1

Y1 - 2013/12/1

N2 - With the increasing availability of metropolitan transportation data, such as those from vehicle Global Positioning Systems (GPSs) and road-side sensors, it has become viable for authorities, operators, and individuals to analyze the data for better understanding of the transportation system and, possibly, improved utilization and planning of the system. We report our experience in building the Visual Analytics for Intelligent Transportation (VAIT) system, which is the first system on real-life large-scale data sets for intelligent transportation. Our key observation is that metropolitan transportation data are inherently visual as they are spatio-temporal around road networks. Therefore, we visualize and manage traffic data, together with digital maps, and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAIT on real-world taxi GPS and meter data sets from 15 000 taxis running for two months in a Chinese city of over 10 million people. We discuss the technical challenges in data calibration, storage, visualization, and query processing and offer first-hand lessons learned from developing the system. Based on our extensive empirical experiment results, VAIT beats state-of-the-art methods and systems in terms of scalability, efficiency, and effectiveness and offers us an easy-to-use, efficient, and scalable platform to shed more light on intelligent transportation research.

AB - With the increasing availability of metropolitan transportation data, such as those from vehicle Global Positioning Systems (GPSs) and road-side sensors, it has become viable for authorities, operators, and individuals to analyze the data for better understanding of the transportation system and, possibly, improved utilization and planning of the system. We report our experience in building the Visual Analytics for Intelligent Transportation (VAIT) system, which is the first system on real-life large-scale data sets for intelligent transportation. Our key observation is that metropolitan transportation data are inherently visual as they are spatio-temporal around road networks. Therefore, we visualize and manage traffic data, together with digital maps, and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAIT on real-world taxi GPS and meter data sets from 15 000 taxis running for two months in a Chinese city of over 10 million people. We discuss the technical challenges in data calibration, storage, visualization, and query processing and offer first-hand lessons learned from developing the system. Based on our extensive empirical experiment results, VAIT beats state-of-the-art methods and systems in terms of scalability, efficiency, and effectiveness and offers us an easy-to-use, efficient, and scalable platform to shed more light on intelligent transportation research.

UR - http://www.scopus.com/inward/record.url?scp=84890432967&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84890432967&partnerID=8YFLogxK

U2 - 10.1109/TITS.2013.2263225

DO - 10.1109/TITS.2013.2263225

M3 - Article

AN - SCOPUS:84890432967

VL - 14

SP - 1586

EP - 1596

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 4

M1 - 6522454

ER -